Proactive Replication of Software Containers Using Geographic Location Affinity to Predicted Clusters in a Distributed Computing Environment

2. The apparatus of claim 1 wherein the geographically-distributed container platform comprises a cloud computing platform.

5. The apparatus of claim 4 wherein the geographic location of the given cluster comprise a cluster midpoint location.

6. The apparatus of claim 1 wherein generating the cluster pattern data comprises identifying the one or more geographic clusters utilizing a machine learning clustering algorithm.

7. The apparatus of claim 6 wherein the machine learning clustering algorithm comprises a K-means clustering algorithm.

8. The apparatus of claim 6 wherein the machine learning clustering algorithm comprises at least one of: a mini-batch K-means clustering algorithm, a hierarchical clustering algorithm, a density-based spatial clustering of application with noise algorithm, and a mean shift clustering algorithm.

9. The apparatus of claim 1 wherein the neural network model comprises a long short term memory neural network model.

10. The apparatus of claim 1 wherein the calculated network distances are based at least in part on geographic distances between the predicted geographic cluster and each of the subset of the plurality of container host devices.

11. The apparatus of claim 10 wherein the calculated network distances are further based at least in part on predictions of available network bandwidth and network latency between the predicted geographic cluster and each of the subset of the plurality of container host devices for the given time slice of the given time interval.

12. The apparatus of claim 1 wherein calculating the network distance from the predicted geographic cluster to each of the subset of the plurality of container host devices comprises utilizing a Haversine distance computation algorithm.

13. The apparatus of claim 1 wherein proactively replicating the at least one software container instance in the selected container host device prior to the given time slice of the given time interval comprises redirecting network traffic from at least one other container host device hosting the at least one software container instance to the selected container host device.

15. The computer program product of claim 14 wherein the neural network model comprises a long short term memory neural network model.

17. The method of claim 16 wherein the neural network model comprises a long short term memory neural network model.

18. The apparatus of claim 9 wherein the long short term memory neural network model comprises a cell state providing long term memory and a hidden state providing short term memory, wherein the hidden state and the cluster pattern time series data are applied to an activation function to produce a first output, wherein a forget filter is applied to the cell state to produce a second output, and wherein the first output and the second output are combined to generate the at least one prediction value indicating the likelihood of cluster formation for the given time slice.

19. The computer program product of claim 15 wherein the long short term memory neural network model comprises a cell state providing long term memory and a hidden state providing short term memory, wherein the hidden state and the cluster pattern time series data are applied to an activation function to produce a first output, wherein a forget filter is applied to the cell state to produce a second output, and wherein the first output and the second output are combined to generate the at least one prediction value indicating the likelihood of cluster formation for the given time slice.

20. The method of claim 17 wherein the long short term memory neural network model comprises a cell state providing long term memory and a hidden state providing short term memory, wherein the hidden state and the cluster pattern time series data are applied to an activation function to produce a first output, wherein a forget filter is applied to the cell state to produce a second output, and wherein the first output and the second output are combined to generate the at least one prediction value indicating the likelihood of cluster formation for the given time slice.